{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import isoweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Year</th>\n",
       "      <th>Wheat</th>\n",
       "      <th>Wages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1570</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.05</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1575</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1580</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5.12</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1585</td>\n",
       "      <td>41.5</td>\n",
       "      <td>5.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1590</td>\n",
       "      <td>47.0</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1595</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1600</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1605</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1610</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1615</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1620</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>1625</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>1630</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>1635</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1640</td>\n",
       "      <td>39.0</td>\n",
       "      <td>6.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>1645</td>\n",
       "      <td>53.0</td>\n",
       "      <td>6.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1650</td>\n",
       "      <td>42.0</td>\n",
       "      <td>6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>1655</td>\n",
       "      <td>40.5</td>\n",
       "      <td>6.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1660</td>\n",
       "      <td>46.5</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>1665</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1670</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1675</td>\n",
       "      <td>43.0</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1680</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>1685</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1690</td>\n",
       "      <td>40.0</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>1695</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>1700</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1705</td>\n",
       "      <td>32.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>1710</td>\n",
       "      <td>44.0</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>1715</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1720</td>\n",
       "      <td>29.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>1725</td>\n",
       "      <td>39.0</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>1730</td>\n",
       "      <td>26.0</td>\n",
       "      <td>13.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>1735</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>1740</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>1745</td>\n",
       "      <td>27.5</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>1750</td>\n",
       "      <td>31.0</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>1755</td>\n",
       "      <td>35.5</td>\n",
       "      <td>15.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>1760</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>1765</td>\n",
       "      <td>43.0</td>\n",
       "      <td>17.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>1770</td>\n",
       "      <td>47.0</td>\n",
       "      <td>18.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>1775</td>\n",
       "      <td>44.0</td>\n",
       "      <td>19.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>1780</td>\n",
       "      <td>46.0</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>1785</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>1790</td>\n",
       "      <td>47.5</td>\n",
       "      <td>25.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>1795</td>\n",
       "      <td>76.0</td>\n",
       "      <td>27.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>1800</td>\n",
       "      <td>79.0</td>\n",
       "      <td>28.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>1805</td>\n",
       "      <td>81.0</td>\n",
       "      <td>29.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>1810</td>\n",
       "      <td>99.0</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>1815</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>1820</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>1821</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  Year  Wheat  Wages\n",
       "0            1  1565   41.0   5.00\n",
       "1            2  1570   45.0   5.05\n",
       "2            3  1575   42.0   5.08\n",
       "3            4  1580   49.0   5.12\n",
       "4            5  1585   41.5   5.15\n",
       "5            6  1590   47.0   5.25\n",
       "6            7  1595   64.0   5.54\n",
       "7            8  1600   27.0   5.61\n",
       "8            9  1605   33.0   5.69\n",
       "9           10  1610   32.0   5.78\n",
       "10          11  1615   33.0   5.94\n",
       "11          12  1620   35.0   6.01\n",
       "12          13  1625   33.0   6.12\n",
       "13          14  1630   45.0   6.22\n",
       "14          15  1635   33.0   6.30\n",
       "15          16  1640   39.0   6.37\n",
       "16          17  1645   53.0   6.45\n",
       "17          18  1650   42.0   6.50\n",
       "18          19  1655   40.5   6.60\n",
       "19          20  1660   46.5   6.75\n",
       "20          21  1665   32.0   6.80\n",
       "21          22  1670   37.0   6.90\n",
       "22          23  1675   43.0   7.00\n",
       "23          24  1680   35.0   7.30\n",
       "24          25  1685   27.0   7.60\n",
       "25          26  1690   40.0   8.00\n",
       "26          27  1695   50.0   8.50\n",
       "27          28  1700   30.0   9.00\n",
       "28          29  1705   32.0  10.00\n",
       "29          30  1710   44.0  11.00\n",
       "30          31  1715   33.0  11.75\n",
       "31          32  1720   29.0  12.50\n",
       "32          33  1725   39.0  13.00\n",
       "33          34  1730   26.0  13.30\n",
       "34          35  1735   32.0  13.60\n",
       "35          36  1740   27.0  14.00\n",
       "36          37  1745   27.5  14.50\n",
       "37          38  1750   31.0  15.00\n",
       "38          39  1755   35.5  15.70\n",
       "39          40  1760   31.0  16.50\n",
       "40          41  1765   43.0  17.60\n",
       "41          42  1770   47.0  18.50\n",
       "42          43  1775   44.0  19.50\n",
       "43          44  1780   46.0  21.00\n",
       "44          45  1785   42.0  23.00\n",
       "45          46  1790   47.5  25.50\n",
       "46          47  1795   76.0  27.50\n",
       "47          48  1800   79.0  28.50\n",
       "48          49  1805   81.0  29.50\n",
       "49          50  1810   99.0  30.00\n",
       "50          51  1815   78.0    NaN\n",
       "51          52  1820   54.0    NaN\n",
       "52          53  1821   54.0    NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\")\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Year</th>\n",
       "      <th>Wheat</th>\n",
       "      <th>Wages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>1815</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>1820</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>1821</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  Year  Wheat  Wages\n",
       "50          51  1815   78.0    NaN\n",
       "51          52  1820   54.0    NaN\n",
       "52          53  1821   54.0    NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous supprimons ces lignes qui ne contiennent pas des données valables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Year</th>\n",
       "      <th>Wheat</th>\n",
       "      <th>Wages</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <td>1</td>\n",
       "      <td>1565</td>\n",
       "      <td>41.0</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1570</td>\n",
       "      <td>45.0</td>\n",
       "      <td>5.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1575</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1580</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1585</td>\n",
       "      <td>41.5</td>\n",
       "      <td>5.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1590</td>\n",
       "      <td>47.0</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1595</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1600</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1605</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1610</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1615</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1620</td>\n",
       "      <td>35.0</td>\n",
       "      <td>6.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>1625</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>1630</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>1635</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1640</td>\n",
       "      <td>39.0</td>\n",
       "      <td>6.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>1645</td>\n",
       "      <td>53.0</td>\n",
       "      <td>6.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1650</td>\n",
       "      <td>42.0</td>\n",
       "      <td>6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>1655</td>\n",
       "      <td>40.5</td>\n",
       "      <td>6.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1660</td>\n",
       "      <td>46.5</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>1665</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1670</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1675</td>\n",
       "      <td>43.0</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1680</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>1685</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1690</td>\n",
       "      <td>40.0</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>1695</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>1700</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1705</td>\n",
       "      <td>32.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>1710</td>\n",
       "      <td>44.0</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>1715</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1720</td>\n",
       "      <td>29.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>1725</td>\n",
       "      <td>39.0</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>1730</td>\n",
       "      <td>26.0</td>\n",
       "      <td>13.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>1735</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>1740</td>\n",
       "      <td>27.0</td>\n",
       "      <td>14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>1745</td>\n",
       "      <td>27.5</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>1750</td>\n",
       "      <td>31.0</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>1755</td>\n",
       "      <td>35.5</td>\n",
       "      <td>15.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>1760</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>1765</td>\n",
       "      <td>43.0</td>\n",
       "      <td>17.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>1770</td>\n",
       "      <td>47.0</td>\n",
       "      <td>18.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>1775</td>\n",
       "      <td>44.0</td>\n",
       "      <td>19.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>1780</td>\n",
       "      <td>46.0</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>1785</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>1790</td>\n",
       "      <td>47.5</td>\n",
       "      <td>25.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>1795</td>\n",
       "      <td>76.0</td>\n",
       "      <td>27.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>1800</td>\n",
       "      <td>79.0</td>\n",
       "      <td>28.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>1805</td>\n",
       "      <td>81.0</td>\n",
       "      <td>29.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>1810</td>\n",
       "      <td>99.0</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  Year  Wheat  Wages\n",
       "0            1  1565   41.0   5.00\n",
       "1            2  1570   45.0   5.05\n",
       "2            3  1575   42.0   5.08\n",
       "3            4  1580   49.0   5.12\n",
       "4            5  1585   41.5   5.15\n",
       "5            6  1590   47.0   5.25\n",
       "6            7  1595   64.0   5.54\n",
       "7            8  1600   27.0   5.61\n",
       "8            9  1605   33.0   5.69\n",
       "9           10  1610   32.0   5.78\n",
       "10          11  1615   33.0   5.94\n",
       "11          12  1620   35.0   6.01\n",
       "12          13  1625   33.0   6.12\n",
       "13          14  1630   45.0   6.22\n",
       "14          15  1635   33.0   6.30\n",
       "15          16  1640   39.0   6.37\n",
       "16          17  1645   53.0   6.45\n",
       "17          18  1650   42.0   6.50\n",
       "18          19  1655   40.5   6.60\n",
       "19          20  1660   46.5   6.75\n",
       "20          21  1665   32.0   6.80\n",
       "21          22  1670   37.0   6.90\n",
       "22          23  1675   43.0   7.00\n",
       "23          24  1680   35.0   7.30\n",
       "24          25  1685   27.0   7.60\n",
       "25          26  1690   40.0   8.00\n",
       "26          27  1695   50.0   8.50\n",
       "27          28  1700   30.0   9.00\n",
       "28          29  1705   32.0  10.00\n",
       "29          30  1710   44.0  11.00\n",
       "30          31  1715   33.0  11.75\n",
       "31          32  1720   29.0  12.50\n",
       "32          33  1725   39.0  13.00\n",
       "33          34  1730   26.0  13.30\n",
       "34          35  1735   32.0  13.60\n",
       "35          36  1740   27.0  14.00\n",
       "36          37  1745   27.5  14.50\n",
       "37          38  1750   31.0  15.00\n",
       "38          39  1755   35.5  15.70\n",
       "39          40  1760   31.0  16.50\n",
       "40          41  1765   43.0  17.60\n",
       "41          42  1770   47.0  18.50\n",
       "42          43  1775   44.0  19.50\n",
       "43          44  1780   46.0  21.00\n",
       "44          45  1785   42.0  23.00\n",
       "45          46  1790   47.5  25.50\n",
       "46          47  1795   76.0  27.50\n",
       "47          48  1800   79.0  28.50\n",
       "48          49  1805   81.0  29.50\n",
       "49          50  1810   99.0  30.00"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1 = data.dropna().copy()\n",
    "\n",
    "data_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5.00\n",
       "1      5.05\n",
       "2      5.08\n",
       "3      5.12\n",
       "4      5.15\n",
       "5      5.25\n",
       "6      5.54\n",
       "7      5.61\n",
       "8      5.69\n",
       "9      5.78\n",
       "10     5.94\n",
       "11     6.01\n",
       "12     6.12\n",
       "13     6.22\n",
       "14     6.30\n",
       "15     6.37\n",
       "16     6.45\n",
       "17     6.50\n",
       "18     6.60\n",
       "19     6.75\n",
       "20     6.80\n",
       "21     6.90\n",
       "22     7.00\n",
       "23     7.30\n",
       "24     7.60\n",
       "25     8.00\n",
       "26     8.50\n",
       "27     9.00\n",
       "28    10.00\n",
       "29    11.00\n",
       "30    11.75\n",
       "31    12.50\n",
       "32    13.00\n",
       "33    13.30\n",
       "34    13.60\n",
       "35    14.00\n",
       "36    14.50\n",
       "37    15.00\n",
       "38    15.70\n",
       "39    16.50\n",
       "40    17.60\n",
       "41    18.50\n",
       "42    19.50\n",
       "43    21.00\n",
       "44    23.00\n",
       "45    25.50\n",
       "46    27.50\n",
       "47    28.50\n",
       "48    29.50\n",
       "49    30.00\n",
       "Name: Wages, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y1 = data_1['Wages'].astype(float)\n",
    "Y2 = data_1['Wheat'].astype(float)\n",
    "X1 = data_1['Year'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x7f999cf31cf8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X1,Y1,'r')\n",
    "plt.fill_between(X1, Y1,color='#539ecd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 50 artists>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(X1,Y2,color='green')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 50 artists>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax=plt.subplots()\n",
    "ax.plot(X1,Y1,'r')\n",
    "plt.fill_between(X1, Y1,color='#539ecd')\n",
    "ax.bar(X1,Y2,color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax=plt.subplots()\n",
    "ax.plot(X1,Y1)\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('shillings par semaine')\n",
    "\n",
    "ax1 = ax.twinx()\n",
    "ax1.bar(X1,Y2,color = 'black')\n",
    "ax1.set_ylabel('shillings pour un quart de boisseau de blé')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.121951\n",
       "1     0.112222\n",
       "2     0.120952\n",
       "3     0.104490\n",
       "4     0.124096\n",
       "5     0.111702\n",
       "6     0.086563\n",
       "7     0.207778\n",
       "8     0.172424\n",
       "9     0.180625\n",
       "10    0.180000\n",
       "11    0.171714\n",
       "12    0.185455\n",
       "13    0.138222\n",
       "14    0.190909\n",
       "15    0.163333\n",
       "16    0.121698\n",
       "17    0.154762\n",
       "18    0.162963\n",
       "19    0.145161\n",
       "20    0.212500\n",
       "21    0.186486\n",
       "22    0.162791\n",
       "23    0.208571\n",
       "24    0.281481\n",
       "25    0.200000\n",
       "26    0.170000\n",
       "27    0.300000\n",
       "28    0.312500\n",
       "29    0.250000\n",
       "30    0.356061\n",
       "31    0.431034\n",
       "32    0.333333\n",
       "33    0.511538\n",
       "34    0.425000\n",
       "35    0.518519\n",
       "36    0.527273\n",
       "37    0.483871\n",
       "38    0.442254\n",
       "39    0.532258\n",
       "40    0.409302\n",
       "41    0.393617\n",
       "42    0.443182\n",
       "43    0.456522\n",
       "44    0.547619\n",
       "45    0.536842\n",
       "46    0.361842\n",
       "47    0.360759\n",
       "48    0.364198\n",
       "49    0.303030\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y3=Y1/Y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Year')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bux/fE/GcQMnn0XoKcHJeYTxVRGMhKzWJjJQkyyuQGrv6eLu2g/ULJ76XACeHj6KZ44gnUx/mlVIx5+r3cLjZRXv6QMTzAt9ucyOM5wcmf2M5nxAN32Y71hPYnt/fBMD6GMwnAKQ7kkhNtunwkVIqfu093okxvg/99p7RP6wCQSE/wvDRwmJfTyEe5hMCSnLSqO+wGhQaKc5KZck4axtZkZ/ACWwaFJQ6Deyq6wj+HrqJzXAn6x6NvkxzWUk2X75iCVetLJ24Bp4iqzuwDQx6eelAM+sXFYbN2J4oeQlc/0iDglKngd31ncEtLccKCjaBnLTRd1Kz2YSPnFtJdoRzJltpThqtLje97sib7Ww50kpXv4f1C2MzdBSQn5G49Y80KCh1GthV38lZlXnYbRIxKLS43OSmOya8nlGslQZWII0xr/DcvkYcdhvvnFcQ0/YkcvlsDQpKTXP9nkEOnuhixewcZuemcShST6HbHZNlmrFmdV+FZ/c1ctacPJwpsV1jEyifnYj1jzQoKDXNHWjoxuM1LCvJprLAyeGmyMNHo2Uzx7PAtpyRgsLRlh6qm1wxHzoC35xMv8dLzxjDWfFIg4JS09zuet8k89KSLCoLMjjc7Br1G2xrjztijkK8KsrybbYTKSg8t78RICalLYZL5AQ2DQpKTXO76zvJTEmiLC+dykInvQODnOgMP96dqD2FZLuNWbnpbN59gs6+8LkYz+5rZE6Bk4qC2C+lTeT6RzENCiJyiYjsF5EqEfl8hPPWisigiFwTy/YodTraVd/B4pIsbDZhjv8D8VBz94jzBr2GtgTtKQB89aqlVDd18y+/3ErfwNBhmx63h1cPtcQsYW24QGBNxMnmMYOCiHzAyrEw59iBnwCXAkuA60RkySjnfQPYbKXBSinrBr2Gvcc7WVaSDUClPyiEW4HU3uPGmJF7MyeK9Qtn8J0PruCNI63c/pu38Ax6g4+9UtWC2+OdlPkE8CWvwfTtKXzB4rHh1gFVxphDxhg38DvgqjDnfQL4I9Bo4ZpKqSgcauqmb8DL0hJf9m5xViqpybawk83BEhcJGhQArlpZyl0bl/L03hN87o87gxvvPLu/EafDzrrKvElpR6CgYCLOKYy6LktELgUuA0pF5IchD2UBVrYUKgWOhfxdC5w17DVKgauBC4G1FtuslLIoUOJ6aakvKNhsQkW+M2xPIVjiIkI2cyK48ZwKWl0DfO/pA+SmJ/PFyxfz/L5Gzp1fgCNpcqZRnQ47jiTb9AoKQD2wFdgIbAs53gXcaeHa4bJfhi95+D7wOWPMYKSUcxG5FbgVoKyszMJLK6XAV94iJcnGvJDidXMKnew73jXi3JMlLhK3pxBwx0XzaOtxc//Lh2nvHaC+o49/3zB/0l5fRChwOmhJwKJ4owYFY8wOYIeI/MYYE7m0Yni1wOyQv2fhCzSh1gC/8weEAuAyEfEYY/4yrC33AfcBrFmzJvGyQZSaIrvrO1lUnEmS/eQ35MoCJ0/uPsHAoJfkkOMtForhJQoR4ctXLKGjd4A/bKsFmLT5hIC8DEewFHkisZLWVyEi/4tvsjg1cNAYM2eM520B5otIJVAHXAtcH3qCMaYy8LuIPAg8PjwgKKXGxxjD7voOrlhRMuR4ZUEGHq+htq03OPEM1spmJxKbTfjmNcvxeA297kFmZKWO/aQJlJeglVKtBIVfAF8BvgesB24m/NDQEMYYj4jcjm9VkR34uTFmt4jc5n/83nG3Wik1ptq2Xjr7PMFJ5oCTK5C6RwSFzJSkSRt3nwzJdhs/um7VlLx2vtNBdePIpb/xzkpQSDPGPCMiYoypAf5bRF7CFygiMsZsAjYNOxY2GBhj/tlCW5RSFgXKZQeWowYEcxWaXFy46OTxVpc74jacKjp5zsSslGolKPSJiA046P/mXwdM7uCcUipqgXLZgU1xAnKdDnLSk0esQErUbOZ4led00DswSI/bExd7WVtlpZ/4SSAduANYDdwI3BTLRimlTt2u+g7mz8ggNdk+4rHKgpHLUltciZvNHI8KAqUuEmwF0phBwRizxRjTbYypNcbcbIx5nzHmtclonFJq/HbXd7KkJPyWk+GCQqurX3sKEyiwe12iDSGN2acRkQXAfwDloecbYy6MYbuUUqegsbOPpq7+EfMJAXMKnPzpzTp63YOkOewYY/zDR4mduBZP8hK0UqqVga5HgXuB/wckXnFwpU5DwUzmUXsKvmS2Iy0uFs/Morvfw8CgIc8ZP1tsJrrAUFyi1T+yEhQ8xph7Yt4SpdSECeyhEGn4CHyF8RbPzArJZtaewkQ5Wf8osRLYItU+ClSO+quI/BvwZyB4d8aY1hi3TSk1TrvqOqnITyczNfw3/4qCdOBktdRgNrPOKUyYzJQkHHZbwk00R+opbMNXqyiQqPYfIY8ZYKyMZqXUFNl9vIPls3JGfTzdkcTM7FQO+aultnZPn7pH8UJEyPPv1ZxIItU+qhztMaVU/OroGeBYay/XrYtcPNK3AsmXcTudiuHFk0RMYLOyyc7HRSQn5O9c/3CSUioO7T4ePpN5uIqQZaktGhRiIj9j7J6CMYaDJ7p4+PUa7vz9dq740UtUNY6sYjtZrEw0/4sx5ieBP4wxbSLyL8BPY9cspdR47a6LvPIoYE6Bk7aeAdpcbtp63KQk2Uh3jEx0U+OX73Tw+uFWPnDvK+SkO8hNTybX6SA33YEx8ObRNrYeaaWtx1eIuiAjhRZXP5t2NnDHRZljXD02rAQFm7/ukYHg9pn6dUKpOLW7voOZ2ankZ0ReSRRcgdTioqXbl80caV8TFb3r1pXR7/HS1uPmWGsPb9e6aesZwO3xbRVakZ/OhsVFrK3MY21FHhX56Vz6g5fYcmTq1vFYCQqbgUdE5F58E8y3AU/EtFVKqXHbVd85Zi8BQoJCk8uXzazF8CbcWXPyOWtO/pBjxhh63IN4Bg3Z6SNXh62pyOXPb9bhGfQO2Qdjslh5xc8BzwIfAz4OPAN8NpaNUkqNT79nkENN3SyeOXZQmJ2Xjt0mHG52aTbzJBIRnClJYQMCwNqKPFzuQfaG2R1vMozZUzDGeIF7/D9KqThW29aL10BFvnPMc5PtNsry0jnc7KLF5R6yt4KaOusqfSliW460csasyIsFYsHK6qP5IvIHEdkjIocCP5PROKVUdI629ABQnp9u6fzKAieHml20aU8hbszMTqM0J23K5hWsDB/9Al8vwYNv57VfAb+OZaOUUuNT0+JbYloWRVCoburG5R6cFnszTxfrKvPYcqQN//qeSWUlKKQZY54BxBhTY4z5b0ArpCoVh2pae0h32CkcY+VRQGWBM7gSRnMU4sfaijyau/s54u/5TSYrQWHIzmsicjW685pSceloSw9leemWl5bOCZlH0KAQP9ZW5AKw5fDkDyGNZ+e1G9Cd15SKSzWtvqBgVWXhyaCgxfDix7wZGeSmJ0/JvIKV1Udb/L92AzfHtjlKqfHyeg1HW3tYv7DQ8nOKMlNJS7bTOzBIrgaFuCEirKnIm5KgMGpPQUR+ISI/F5HvTWaDlFLjc6KrD7fHS5mF5agBNptQ4R9C0p5CfFlbkcuRlh4au/om9XUjDR89CPwSeGRymqKUOhU1geWoUQwfgW9ewW4TskbZe0FNjbUVvnyFrUfaJvV1I5XOfmEyG6KUOjXR5igEXL58Js4UOzab1j2KJ0tLsklNtvHG4VYuO2PmpL1upJ3XduKrdRSWMWZ5TFqklBqXmlYXdptQkpMW1fMuO2PmpH7oKGscSTZWzc5la83kzitEmmi+wv/Pj/v/GUhY+xAw+YtnlVIR1bT0UJqTRvIUFFFTsbG2Mo8fP3uQrr6BUbdWnWij/tfjT1SrAd5pjPmsMWan/+fzwHsmpXVKKcuOtvZEPXSk4tvaily8Bt482j5pr2nlK4VTRM4N/CEi7wC0cpZSceZolDkKKv6dWZaL3SZsncSlqVb2U7gF+LmIZOObY+gAPhLTVimlotLRO0B7z4D2FKYZZ0oSS0uyeGMSM5utJK9tA1aISBa++kcdsW+WUioagZVHZXnaiZ9u1pTn8fDrNbg9XhxJsZ8vsvwKxphODQhKxaeaVl91VO0pTD/rKnPp93jZWTc5H7+6TEGpOPbG4Vb+9+978Qx6I55XE+wpaFCYbtZUnNx0ZzJEDAoiYvNPLCulJtmf36rlQ/e/xs9eODTmt8SjLT0UZKTgTLEyTagSSUFGCnMKnJM22RwxKPi34vzOpLREKQX4Nnb//tMHuPP3Ozij1Lcd47aayKUOalpdOnQ0ja2t8G264/XGftMdK8NHT4rI+8VqgfYQInKJiOwXkSoR+XyYx68SkbdFZLuIbA1d+qrU6cjt8fLpR3fw/acPcs3qWfzu1nMoy0sfs/7N0ZaeqGseqcSxtjKPjt4BDjZ2x/y1rPQ1P4UvL8EjIn2AAMYYkxXpSSJiB34CXAzUAltE5DFjzJ6Q054BHjPGGBFZjq/43qJx3IdSCa+jZ4B/fWgrrx1q5dMXL+D2C+f5SiiX5/LiwSaMMWE3z+n3DHK8s8/yFpwq8QQ33TnSysLizJi+1pg9BWNMpjHGZoxxGGOy/H9HDAh+64AqY8whY4wb+B1w1bBrd5uTm5A6iVBrSanprLath/fd8w/erGnnB9eu5BMXzQ8GgDUVeTR3u4OTycMda+3FGF15NJ2V5aUzf0YGrn5PzF8rUkG8RcaYfSJyZrjHjTFvjnHtUuBYyN+1wFlhXudq4H/xbfF5+ShtuRW4FaCsrGyMl1Uq8fzg6YMc7+jjoY+exbrKvCGPrfF/S9xa0xbc+yDUUf9yVM1RmL5EhCfvPN/yNqunItLw0afwfRCHm2g2wIVjXDtc60f0BIwxfwb+LCLnA3cDG8Kccx9wH8CaNWu0N6GmnaOtPSwryR4READmFWaQlZrE1iOtXLN61ojHa8ZZMlsllskICBB5P4Vb/f9cP85r1wKzQ/6eBdRHeL0XRWSuiBQYY5rH+ZpKJaTjHX2snJ0T9jGbTVhdnsvWUVYg1bT04HTYdec0NSHGnFMQkWQRuUNE/uD/uV1ErNRw3QLMF5FKEXEA1wKPDbv2vMCqJv8wlQNoif42lEpcxhgaOvqYmZM66jlrKvKoauymzeUe8djR1h7K8p2T9k1STW9WlqTeA6wGfur/We0/FpExxgPcDmwG9gKPGGN2i8htInKb/7T3A7tEZDu+lUr/FDLxrNRpocXlxj3oZWZWhKBQ7ptXCJevUNPi0uWoasJYWZK61hizIuTvZ0Vkh5WLG2M2AZuGHbs35PdvAN+wci2lpquGDt/G7DMj7Ji2YnYOyXZha00bG5YUBY97vYZjbb1sWFw06nOVioaVnsKgiMwN/CEic4DB2DVJqdNLfXsvADOzR+8ppCbbWVaazbZhWzM2dPbh9ng1R0FNGCtB4T+A50TkeRF5AXgW+HRsm6VU/PN6Dc/ta+RURzwbOv09hezIeyuvKc9lR20H/Z6T38mCK490OaqaIFaS154B5gN3+H8WGmOei3XDlIp3zx9o5OYHt/DSwVNbLFff3keyXcZcPbS6PA+3x8uukOJ4R7VktppgVlYf7cCXs+AyxuwwxvTHvllKxb/tx3wfznuOd57SdRo6einOTsVmi7x6KJjEFlIHqaalhySbRBx6UioaVoaPNuKbQ3hERLaIyGdERNOK1Wkv8I193ykGhfqOPmZmRR46Al8J5coCJ1tCg0JrD7Ny00iy69YoamJYGT6qMcZ80xizGrgeWA4cjnnLlIpzgT0O9jV0ndJ1jnf0RsxRCLW6PJc3j7YF5zGOtvhyFJSaKJa+XohIhYh8Fl9Ru0XAZ2PaKqXi3InOPpq6+slKTaK6qZuBMXZGG43XazjR0U+xxeGftRW5tLrcHGr2zSVojoKaaFbmFF4H/gTYgQ8YY9YZY3TjHXVa21nr6yVsXFnCwKDhUJNrXNcJJK6VjLHyKGB1ua820tYjrbT3uOns8+gks5pQVnoKNxljzjTG/K8x5lDMW6RUAthV34EIXL3KV6BuX8P45hUCiWtWewpzC53kpiez9Uib7susYsJKRvMHw9VUMcbcNfHNUSox7KrrYG5hBmeUZpNkE/Y1dA3dLMSi+g5f4prVnoIrRx8IAAAdB0lEQVSIsLo8j201bZy3oBCAcp1TUBPISk/BFfIzCFwKVMSwTUrFvZ11HSwrycKRZGPejIxxr0CKtqcAvqWph5pdvHXUtwpJewpqIo3ZUxg+fyAi32ZYtVOlTieNXX2c6OxnWWk2AAuLM9lyuHWMZ4VX39GLw26Lqux1oDjeX3fUMyMzhTSHfVyvrVQ441ncnA7MmeiGKJUoAvkJZ/iDwqLiLOo7+ujoGYj6Wg0dfRRlp4yZuBbqjFnZOJJsNHe7tZegJpyV1Uc7ReRt/89uYD/wg9g3Tan4tLO2ExFYGgwKvo3U95+IPl/heHvfmDWPhktJsrPc/9paCE9NNCsTzVeE/O4BTvj3SlDqtLSzroPKAicZKb7/fRbN9AWFfQ2dYbfTjOR4Zy+ry3KjbsPqCt9ObFoIT000SxnNQA5wJXA1sCTWjVIqnu2q6wgOHQEUZ6WSlZoUdWaz1+vbca04yp4CwFp/voLmKKiJZmX46N+Bh4EZ/p+HReQTsW6YUvGoqaufhs6+IUFBRFg0MyvqFUgtLjcDg4YSiyUuQp2/oJD/eM/CIRvuKDURrAwf3QKcZYxxAYjIN4BXgR/FsmFKxaNd9b5J5qUl2UOOLy7O5I9v1uH1GsuTxsf9OQrFEbbhHI0jycbH18+L+nlKjcXK6iNh6E5rg/5jSp12dvnLWywtzRpyfGFxFt39Hur8u6hZcdyfo1ASYRtOpSablZ7CL4DXReTP/r/fCzwQuyYpFb8Ck8xZqclDjp+cbO5itsVlosf9ASSaxDWlYs3KRPN3gZuBVqANuNkY8/1YN0ypeLSrriOYtBZqQZE/KEQxr3C8sy/qxDWlYm3UnoKIpAK3AfOAncBPdSmqOp21dPdT39HHPw8bOgLISEmiLC+dfVHkKhxv76M4O5VwtcWUmiqRegq/BNbgCwiXAt+elBYpFacCm+qE6ymAr9xFND2Fho4+3UZTxZ1IcwpLjDFnAIjIA8Abk9MkpeLT7nrfB/7wlUcBi4szeWbvCfoGBklNHrseUX1Hb7COkVLxIlJPIVjIRYeNlPJtrFOen052WnLYxxcWZ+E1UNXYPea1vF7Dic4+ZurKIxVnIgWFFSLS6f/pApYHfheRU9upXKkEtHOUSeaA0BVIY2l29TMwaHT4SMWdUYePjDFaj1cpvzaXm7r2Xm48p3zUcyrynaQk2SzNKxxv9+UoRFsMT6lYG0/pbKVOOzuHlcsOx24TFhRlWqqWGkhc056CijcaFJSyILjyaJRJ5oCFxZnsPW4lKPgS1zQoqHijQUEpC3bVdVCWl052evhJ5oBFxZk0d/fT3N0f8byGjj4cSTbyNHFNxRkNCkpZsHNYuezRLCr2JbbtH2Oyud6fo6CJayreaFBQ01LfwCAfeXAL22rGt3dyqPYeN7VtvSOK4IVjdQVSQ0fvuKqjKhVrGhTUtLT9WDvP7mvki3/exaDXnNK1dtX5VhNZ6SkUZKRQkJEy5gqk+vY+rY6q4pIGhTj13Sf388jWY1PdjIS1/Vg74PvG/odtp/bv8a2jbcDYk8wBi4ojr0AKJK5pdVQVj2IaFETkEhHZLyJVIvL5MI9/SETe9v+8IiIrYtmeRPKr12r4xt/30e8ZHPtkNcJbR9soy0tnVVkO33nyAK7+8SflP7X3BCtm55BrcVJ4UXEm+xu6Ru2hNHf34/EaSjQoqDgUs6AgInbgJ/iK6S0BrhOR4fs7HwbeZYxZDtwN3Ber9iQSV7+H9p4BWlxuntjVMNXNSUjbj7WzqiyH/7p8MY1d/dz34qFxXae+vZe3azt4z1Lr214uLM6k3+OlpsUV9vFAjsJ49mZWKtZi2VNYB1QZYw4ZY9zA74CrQk8wxrxijGnz//kaMCuG7UkYobt3Pfz60SlsSWI63tHLic5+Vs7OYXV5HpefMZP7XjzEic6+qK/15G5fUH7P0mLLz1k80zchPdpks+YoqHgWy6BQCoQO5tb6j43mFuDv4R4QkVtFZKuIbG1qaprAJsanQFC4eEkRbxxu5UAUNfoVbD/qm09YOTsHgM9eshCP18t3ntwf9bU27z7BvBkZzC3MsPyceTMySHfY2bw7fC9Ps5lVPItlUAi3ADvsIKuIrMcXFD4X7nFjzH3GmDXGmDWFhYUT2MT4VNfmCwqf3DAfh93Gw6/VTHGLEsv2Y+047DaWlPi+sZfnO7npnAoe3VbLnnrrtRzbXG7eONIa1dARQGqynRvOLuevO+o50jxyCOm4Jq6pOBbLoFALzA75exZQP/wkEVkO3A9cZYxpiWF7EkZ9ey9JNmFRcRaXnVHMn96sO6WJ0tPNW8faWVySRUrSyZqOn7hwPlmpyXxt016MsbZE9em9Jxj0mqiGjgI+el4lSXYb9zxfPeKx45q4puJYLIPCFmC+iFSKiAO4Fngs9AQRKQP+BNxojDkQw7YklLr2XoqzU7HbhBvOLqer38NjO0bE07jR0t3PfS9W4xn0TnVT8Ax62VnbwSr/0FFAdnoyd1w0n5ermnn+gLUhyM27T1CSnWopP2G4GZmpXLd2Nn98s3bIHBHA8fZeHTpScStmQcG/Mc/twGZgL/CIMWa3iNwmIrf5T/sykA/8VES2i8jWWLUnkdS391LqT2xaXZ7LouJMHnqtxvI33Mn28OtH+dqmfTy6rXaqm8KBE930DgwG5xNC3Xh2OeX56Xztb3vHDGA9bg8vHWzi3UuLx/2N/tZ3zQXgvheG9hZ8PQVdeaTiU0zzFIwxm4wxC4wxc40x/+M/dq8x5l7/7x81xuQaY1b6f9bEsj2Joq7tZFAQET50djm76zuDCVmx1tk3QHcUw1Uv+r95/+Dpg/QNTG1eReDfUbig4Eiy8flLFnGwsZvfj5EY+ML+Jvo9Xt4d5XxCqNKcNN5/5ix+u+UYjV2+yeXBwI5r2lNQcUozmuPMwKCXhs4+SnNPfpO8elUpToedh16L/fLUnbUdvOubz3HHb9+ydH5n3wBvHWtnXWUeDZ19PDTFk+Lbj7WRm55MeX562McvWVbMuso8vrV5P60u96jX2by7gdz0ZNZV5J1Sez52wVw8g17uf+kw4Btq83h1xzUVvzQoxJkTnX14DcGeAkBGShLvXVXK42/X094T/oPM6zUcaXZR395Le4+bvoHBqIebthxp5fr/9xptPQO8eKCJjt6BMZ/zanULg17Dpy9ewHnzC/jJc1V09Y39vFjZfqydlbNzRh3yERHuvmoZ3X0evvH3fWHPcXu8PLOvkYsWF5FkP7X/RSoKnFy5ooSHXquhzeWmvkN3XFPxTYNCnAksRx1eLO2Gs8vp93j5Q5hx+1erW7jiRy9zwbef5x1ff5aVdz3Foi89wbwv/p1lX9nMO7/+LPe+UB2xZMZLB5u48YHXKcxK4SfXn4nHa3h+f+OY7X3xQBNOh51VZbl85t0LaesZ4IGXD0d51xOjq2+Ag43drJydG/G8hcWZ3HJuJb/feoytR0ZWUX3tUAtdfZ5xrToK5+Pr59HjHuQX/zhMgz9xTeseqXg16h7NamrU+z80QoePwJclu7o8l9+8fpRbzq1ERDjS7OJrm/by5B7fKpn/vnIJqcl2etyD9A4M0uP20OMe5OCJbr7+93389o2j/Odli3n3kqIh36Q3727gE795i7kzMvj1LevITXdQkOHg6b2NXLUyUr4hvHSwmXPmFuBIsrFidg6XLC3m/pcO8+FzKiZ9Hf7O2g6MgZVlI+cThrvjovn8dUc9//WXXTz+iXOH9Ag2724g3WHnvPkFE9KuBUWZXLK0mAdfOcJHz5sDjAz6SsULDQpxJthTCDO8cMPZZdz5+x08sauBbTVt/PLVIyTbbXzm3Qv46HlzSE22j3hOwIsHmrj78T3866+3cc6cfL585RIWz8ziL2/V8elHd3BGaTa/vHldcGexixYVsWnncdweL46k8B3KI80ujrb28NHzKoPHPv3uBTy5p4F7nq/ii5cPL3UVW28FJplnjR0UnClJfPnKpdz20LYhH9Zer+GpPSd414LCiP8+o/Xx9fN4YncD9790iJQkG7lj7OCm1FTR4aM4U9feS77TQZpj5AfSpctmkpuezMcefpMH/nGYq1eV8vxnLuD2C+eP+QF2/oJC/v7v53HXVUvZ29DJ5T98iVse3MKdj2xnbUUuD330rCFbTW5YUkRXv4c3Do++Sc1LB32rjs6ffzLLfH5RJlevmsUvX60J1viZLG8dbWdOgXPMLTMD3rO0iPULC/neUweCbX3rWDuNXf0TNnQUcMasbC5YWEhnn0cT11Rc06AQZ+oibL6SmmznU+9eyIbFRfz19nP55jUrmBHF7l1JdhsfPqeCFz6znpveUcELB5q4YEEhD968joyUoZ3Gc+cVkJps46k9o1dpffFgM7Pz0kas9PnkhvkYY/jhM1WW23aqjDHBSWarRISvblyGx2u4+/E9gK8AXpJNWL9oxoS38RMXzgN0klnFNw0KcaaurWfIyqPhbjy7nPtvWsOycWTZBmSnJ/OVK5ey5YsbeOCmtWF7GWkOO+fOK+SpPSfCrmIaGPTyanUL580vHPGtd3ZeOtevK+ORrcfC1v6Jhbr2Xpq7+y3NJ4Qqy0/n9vXz2LSzgef3N7J5dwPnzM0nO23ih3dWl+fxT2tmc/GS8ec+KBVrGhTiiDGG+va+EZPMsZLrdGCzjT6M8e4lRdR39LEnzNaSbx1tp7vfM2ToKNTHL5yHw27ju09NTvWSSElrY7n1XXOYU+Dk04/s4EhLz4QPHYX6xjXL+ci5lWOfqNQU0aAQR9p6BugdGIyblSnrF81ABJ7ac2LEYy8eaMJuE94xLz/sc2dkpnLzOyt4bEc9+xqsVyYNxxjDc/saeeDlw6PmXmw/2o4jycai4qyor5+SZOfu9y6jxeVGxBcMlTpdaVCII4GVR5GGjyZTYWYKZ5bl8vTekUHhpYNNrJqdQ1bq6MMst54/B4fdxiNbxl8TaV9DJzc+8AY3P7iFux/fwz0vjKw6Cr6ewrKSrFFXSo3lnfMKuP6sMi5ZWhzVPI1S040GhTgSqKYZL0EBYMPiInbVdVIfUumzzeXm7boOzhtl6CggJ93BBQsLefzt+lH3Kx5NU1c/X/jTTi77wUvsrOvgy1cs4coVJXxr836e3Tc0SA0MetlZ1zFm0tpYvnb1Gdxzw+pTuoZSiU6DQhwJBoVJmlOwIjAp+kxIb+HlqmaMgfMWjJ3ctXFlCY1d/bx+yNpWGX0Dg9zzfDXrv/08j2495lsl9R8X8JFzK/nm+5ezZGYW//7b7VQ1dgefs7+hi36PN+pJZqXUSBoU4kh9ey9pyfa4SmyaW+ikssDJkyHzCi8dbCIrNYkVFpLELlpUhNNht7wfxO2/eZNvPLGPs+fksfnO8/nKlUvJSfdlRqc57Nz34TWkJNu49Vdbg7WZAklrw/dQUEpFT4NCHKlr66UkJ74Sm0SEi5cU+esBDWCM4cUDzZw7vwB7hJVLAWkOOxcvKeLvuxpweyLvYbC/oYun9zZyx0Xzuf+mtWH3RS7NSeOeG1ZzrK2HO377FoNew/aj7eQ7HcyKox6WUolKg0Icqe/opTQ3fMnnqbRhcREDg4YXDjRR1dhNQ2ffqEtRw9m4soSO3oHgvguj+cU/DpOabOPmd1REPG9tRR5f3biMFw408c3N+9h+rC1iZVSllHVa+yiO1LX1srQk+iWVsba6PJfc9GSe2nOCE539AJwbRbG4c+cVkpOezGM76tkwynLPlu5+/vRWHdesnkWuhUJ6159Vxp7jHfzshUMAvHeMwn1KKWu0pxAnet2DtLjccbXyKMBuEy5cVMRz+xp5bl8jcwqdzIqiR+NIsnHpspk8tecEPe7wO7r99o2juD3eMXsJob5y5VLWVfo2wdFJZqUmhgaFODFayex4cfGSIjr7PLxc1RzV0FHAxhUl9A4M8vTekXs0uD1efvVqDecvKGR+UablaybbbfzshtX81+WLOWdO+CQ6pVR0NCjEiUgls+PBefMLgolh51tYijrcuso8irJSeGz7yFVIf9tZT2NXPx95Z0XU1811OvjoeXNOeYc0pZSP/p8UJ+rjMEchlDMliXPnFZBsF84ex7dyu024YnkJLxxopKPn5HadxhgeePkw82Zk8K4F0fdAlFITS4NCnKhr78UmUBTHJRb+87LF/PRDq0l3jG99wsYVJQwMGp7YfTx4bGtNG7vqOrn5nRW6ekipOKBBIU7UtfVSnJVKchwPg8ybkXFKZZ+Xz8qmPD99SCLbz18+THZaMu9bNWsimqiUOkXx+wl0mqlr743boaOJIiJctaKEV6tbaOzq41hrD5t3N3D9WWVhd5pTSk0+DQpxoq69N25KZsfSxpUleA387e3j/PKVI9hE+PA55VPdLKWUnyavxYFBr6Ghoy8ucxQm2rwZmSyemcUjW2upbe3hsjNm6vaUSsWR06qn0NjZF9Pr/+6No2z88cscONEV1fMau/rweM1p0VMA34Tz3uOddPV7dBcypeLMaRMUnth1nPO/9Rw/f/kwXgu1/fsGBnng5cNsq2mzdP2dtR186f928XZtB+//6Su8dDBynZ9Qwc11pvmcQsCVK2YCcGZZzri2z1RKxc5pExRWleXyjrkF3PX4Hq697zVqWkbfUP6ZvSe4+HsvcPfje7jp52+wpz7ydpJdfQPc/ts3KchI4W93nEtpbhr//Ist/PaNo5baFo+b68TSrNx0/r/3LuOuq5ZNdVOUUsOcNkGhKCuVB25aw7euWc7e451c8v2X+NWrR4b0Go619vDRX27lll9uJSXJzo+vX0VmahI3P/jGkJ3HQhlj+OKfd3GstYcfXLuKpSXZPHrbOZw7r4Av/GknX9u0d8yeyekWFABuOLucZaXZU90MpdQwp01QAN+SyA+smc2TnzqftZV5fPn/dvOh+1+nqrGbHz5zkA3ffYFXqpv5z8sWsemO87hieQm/uHktPf2D/PMv3ghu6hLqka3HeGxHPZ+6eEGwOFtmajIP3LSGG88u574XD/Gxh7fR6x4ctV317b3kpCfjTNF5f6XU1DqtgkLAzOw0fnnzWr7+vjPYWdfBhu++wHefOsCGJUU88+l3cev5c4N1fhYVZ/GzG1dzuNnFbb/eRr/n5If7gRNdfOWx3bxzXj4fu2DekNdIstu466qlfOmKJTy55wT/dN+rdPaNDCrg31xHV+AopeLAaRkUwNdruHZdGZvvPJ8Pn1POr29Zx0+uPzPs8sh3zCvgm9cs59VDLXzuD2/j9Rp63YN8/OE3yUhJ4nv/tDLsLmQiwi3nVvKzG1azs66DHz59MGxb6tv7TptJZqVUfIvpeIWIXAL8ALAD9xtjvj7s8UXAL4AzgS8aY74dy/aEU5qTZmnC8+pVs6hv7+Nbm/czMyeNNpebg43d/Ooj65iRGble0buXFvPB1bN58JUjXHdW2ZBtJo0x1LX3cs5cLf2slJp6MespiIgd+AlwKbAEuE5Elgw7rRW4A5j0YDAe/3bBXK4/q4x7nq/md1uO8W8XzOV8i5U9P/OehaQm2/mfv+0dcryz10N3v+e0mmRWSsWvWA4frQOqjDGHjDFu4HfAVaEnGGMajTFbgPCD7XFGRLhr41I2rihh/cJC7rx4geXnFmamcMdF83h2XyPP7z+50UxdnJfMVkqdXmI5fFQKHAv5uxY4K4avNymS7DZ+eN2qcT33n99RyW9eP8rdj+/hnfMKSLbbgkHhdMlmVkrFt1j2FMIVxx87lTjchURuFZGtIrK1qcl6pnC8cSTZ+K/Ll1Dd5OKh12qAkM11NCgopeJALINCLTA75O9ZwMi9GC0wxtxnjFljjFlTWJjYu3NdtHgG580v4HtPHaDV5aauvRdHko18p2Oqm6aUUjENCluA+SJSKSIO4FrgsRi+XkIQEb50xRJc7kG+99QB6tp6Kc1JwxZmSatSSk22mM0pGGM8InI7sBnfktSfG2N2i8ht/sfvFZFiYCuQBXhF5JPAEmNM5GJDCW5BUSY3nl3Or149QkFGCguKMqe6SUopBcQ4T8EYswnYNOzYvSG/N+AbVjrtfHLDfP6yvY7Grn4uWJjYQ2JKqenjtM1onmo56Q4+5V/SWpqTPsWtUUopH63ANoWuX1dGQ0dfcH8BpZSaahoUplCS3cZnL1k01c1QSqkgHT5SSikVpEFBKaVUkAYFpZRSQRoUlFJKBWlQUEopFaRBQSmlVJAGBaWUUkEaFJRSSgWJMePa4mDKiEgTUDPV7ZgkBUDzVDdiCuh9n170vidHuTFmzEJrCRcUTicistUYs2aq2zHZ9L5PL3rf8UWHj5RSSgVpUFBKKRWkQSG+3TfVDZgiet+nF73vOKJzCkoppYK0p6CUUipIg8IkEpGfi0ijiOwadvwTIrJfRHaLyDdDjn9BRKr8j70n5PhqEdnpf+yHIiKTeR/Riua+RaRCRHpFZLv/596Q8xPqviH8vYvI70Pu74iIbA95bNq+56Pd93R6z0e575Ui8pr/3raKyLqQx+Lv/TbG6M8k/QDnA2cCu0KOrQeeBlL8f8/w/3MJsANIASqBasDuf+wN4BxAgL8Dl071vU3gfVeEnjfsOgl136Pd+7DHvwN8+XR4zyPc97R5z0f5b/3JQLuBy4Dn4/n91p7CJDLGvAi0Djv8MeDrxph+/zmN/uNXAb8zxvQbYw4DVcA6EZkJZBljXjW+/3p+Bbx3cu5gfKK877AS8b5h1HsHwP/t74PAb/2Hpvt7DoS977Cm0X0bIMv/ezZQ7/89Lt9vDQpTbwFwnoi8LiIviMha//FS4FjIebX+Y6X+34cfTzSj3TdApYi85T9+nv/YdLnvUOcBJ4wxB/1/T/f3PGD4fcP0fs8/CXxLRI4B3wa+4D8el++37tE89ZKAXOBsYC3wiIjMwddtHM5EOJ5oRrvv40CZMaZFRFYDfxGRpUyf+w51HUO/LU/39zxg+H1P9/f8Y8Cdxpg/isgHgQeADcTp+61BYerVAn/ydxPfEBEvvpootcDskPNm4et21vp/H3480YS9b2NMExAYUtomItX4ehXT5b4BEJEk4H3A6pDD0/09D3vf/iHE6fye3wT8u//3R4H7/b/H5futw0dT7y/AhQAisgBw4CuS9RhwrYikiEglMB94wxhzHOgSkbP9Y7MfBv5vapp+SsLet4gUiojdf3wOvvs+NI3uO2ADsM8YEzpMMN3fcwhz36fBe14PvMv/+4VAYNgsPt/vqZ6tP51+8HWZjwMD+L4N3ILvw/AhYBfwJnBhyPlfxLciYT8hqw+ANf7zq4Ef409CjNefaO4beD+wG9+qjDeBKxP1vke7d//xB4Hbwpw/bd/z0e57Or3no/y3fi6wzX9/rwOr4/n91oxmpZRSQTp8pJRSKkiDglJKqSANCkoppYI0KCillArSoKCUUipIg4JSEYjPyyJyacixD4rIE1PZLqViRZekKjUGEVmGLxN1FWAHtgOXGGOqT+GaScYYzwQ1UakJo0FBKQvEt9+DC3ACXcaYu0XkJuDj+BLxXgFuN8Z4ReQ+fOWT04DfG2Pu8l+jFvgZcAnwfWPMo1NwK0pFpLWPlLLmq/iybd3AGn/v4WrgHcYYjz8QXAv8Bvi8MabVX+fnORH5gzFmj/86LmPMO6fiBpSyQoOCUhYYY1wi8nug2xjTLyIb8FV33erfFCuNk2WQrxORW/D9/1WCbzOVQFD4/eS2XKnoaFBQyjqv/wd85Y1/boz5UugJIjIfX0XMdcaYdhF5CEgNOcU1KS1Vapx09ZFS4/M08EERKQAQkXwRKcO3w1YX0OnfQes9Ea6hVNzRnoJS42CM2SkiXwWeFhEbvqqYtwFb8Q0V7QIOAf+YulYqFT1dfaSUUipIh4+UUkoFaVBQSikVpEFBKaVUkAYFpZRSQRoUlFJKBWlQUEopFaRBQSmlVJAGBaWUUkH/P7eTXrcgamDMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X1,Y3)\n",
    "plt.ylabel(\"Pouvoir d'achat\")\n",
    "plt.xlabel('Year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax=plt.subplots()\n",
    "ax1 = ax.twinx()\n",
    "ax1.plot(Y1,'r--', label = \"salaire\")\n",
    "ax1.set_ylabel('salaire')\n",
    "plt.legend()\n",
    "\n",
    "ax.plot(Y2,'b', label = \"prix de blé\")\n",
    "ax.set_ylabel('prix de blé')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax=plt.subplots()\n",
    "ax1 = ax.twinx()\n",
    "ax1.plot(X1,Y1,'r--', label = \"salaire\")\n",
    "ax1.set_ylabel('salaire')\n",
    "plt.legend()\n",
    "\n",
    "ax.plot(X1,Y2,'b', label = \"prix de blé\")\n",
    "ax.set_ylabel('prix de blé')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ce graphique est le plus claire pour moi car il montre la différence entre le prix de blé et le salaire ce que revient à conclure le pouvoir d'achat "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}